The Effect of Swamping on Outlier Detection in Normal Samples

• Activity Number: 76
• Type: Contributed
• Date/Time: Monday, August 12, 2002 : 8:30 AM to 10:20 AM
• Sponsor: General Methodology
• Author(s): Thomas Woolley*+
• Affiliation(s): Samford University
• Keywords: outliers ; outlier methodology ; swamping

Abstract

Statistical outliers have been an issue of concern to researchers for over two centuries and are the focus of this study. There are two approaches to handling multiple outliers, consecutive and block testing. The major problems inherent in these methods, masking and swamping, respectively, are outlined.

The primary aim of this study is to assess the relative susceptibility to swamping of several common block procedures for multiple outliers in univariate samples. In a modified Monte Carlo approach, pseudo-random samples are generated from a unit-normal distribution, and varying numbers of upper outliers are placed in them according to specified criteria. A swamping index is created, which reflects the relative vulnerability of each test to declare a block of outliers, and the most extreme upper outlier, discordant as a unit.

The results of this investigation reveal that the block tests disagree in their respective susceptibilities to swamping depending upon sample size and the prespecified number of outliers assumed to be present. Implications and suggestions for further research are presented.